Cephalalgia : an international journal of headache · 2023
Developing an artificial intelligence-based headache diagnostic model and its utility for non-specialists' diagnostic accuracy.
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Katsuki Masahito, Shimazu Tomokazu, Kikui Shoji, Danno Daisuke, Miyahara Junichi, Takeshima Ryusaku, Takeshima Eriko, Shimazu Yuki, Nakashima Takahiro, Matsuo Mitsuhiro, Takeshima Takao
Аннотация
BACKGROUND: Misdiagnoses of headache disorders are a serious issue. Therefore, we developed an artificial intelligence-based headache diagnosis model using a large questionnaire database in a specialized headache hospital.
METHODS: Phase 1: We developed an artificial intelligence model based on a retrospective investigation of 4000 patients (2800 training and 1200 test dataset) diagnosed by headache specialists. Phase 2: The model's efficacy and accuracy were validated. Five non-headache specialists first diagnosed headaches in 50 patients, who were then re-diagnosed using AI. The ground truth was the diagnosis by headache specialists. The diagnostic performance and concordance rates between headache specialists and non-specialists with or without artificial intelligence were evaluated.
RESULTS: Phase 1: The model's macro-average accuracy, sensitivity (recall), specificity, precision, and F values were 76.25%, 56.26%, 92.16%, 61.24%, and 56.88%, respectively, for the test dataset. Phase 2: Five non-specialists diagnosed headaches without artificial intelligence with 46% overall accuracy and 0.212 kappa for the ground truth. The statistically improved values with artificial intelligence were 83.20% and 0.678, respectively. Other diagnostic indexes were also improved.
CONCLUSIONS: Artificial intelligence improved the non-specialist diagnostic performance. Given the model's limitations based on the data from a single center and the low diagnostic accuracy for secondary headaches, further data collection and validation are needed.
METHODS: Phase 1: We developed an artificial intelligence model based on a retrospective investigation of 4000 patients (2800 training and 1200 test dataset) diagnosed by headache specialists. Phase 2: The model's efficacy and accuracy were validated. Five non-headache specialists first diagnosed headaches in 50 patients, who were then re-diagnosed using AI. The ground truth was the diagnosis by headache specialists. The diagnostic performance and concordance rates between headache specialists and non-specialists with or without artificial intelligence were evaluated.
RESULTS: Phase 1: The model's macro-average accuracy, sensitivity (recall), specificity, precision, and F values were 76.25%, 56.26%, 92.16%, 61.24%, and 56.88%, respectively, for the test dataset. Phase 2: Five non-specialists diagnosed headaches without artificial intelligence with 46% overall accuracy and 0.212 kappa for the ground truth. The statistically improved values with artificial intelligence were 83.20% and 0.678, respectively. Other diagnostic indexes were also improved.
CONCLUSIONS: Artificial intelligence improved the non-specialist diagnostic performance. Given the model's limitations based on the data from a single center and the low diagnostic accuracy for secondary headaches, further data collection and validation are needed.